Adam Thompson
Adam Thompson is a Principal Technical Product Manager at NVIDIA where he focuses on building hardware and software platforms targeting real-time AI, smart sensors, and tying high speed sensor I/O to GPU-accelerated compute. His work advances edge and datacenter/cloud collaborative workloads that integrate Digital Twins of instruments and AI training/fine-tuning deployments.
Adam is also the creator of cuSignal – a GPU-accelerated signal processing library written in Python. With over 400,000 downloads, cuSignal is widely used in the sensor processing communities, and - as of CuPy v13, is fully integrated within CuPy library.
He holds a Masters degree in Electrical and Computer Engineering from Georgia Tech and a Bachelors Degree in Electrical Engineering from Clemson University.
In his free time, Adam enjoys baking, listening to (and discovering!) indie music, modern lit, pour-over coffee techniques, and teaching.
![The speaker's profile picture](/media/avatars/adam_headshot5_7FD8U7V.jpg)
Sessions
From radio telescopes to proton accelerators, scientific instruments produce tremendous amounts of data at equally high rates. To handle this data deluge and to ensure the fidelity of the instruments’ observations, architects have historically written measurements to disk, enabling downstream scientists and researchers to build applications with pre-recorded files. The future of scientific computing is interactive and streaming; how many Nobel Prizes are hidden on a dusty hard drive that a scientist didn’t have time or resources to analyze? In this talk, NVIDIA and the SETI institute will present their joint work in building scalable, real time, high performance, and AI ready sensor processing pipelines at the Allen Telescope Array. Our goal is to provide all scientific computing developers with the tools and tips to connect high speed sensors to GPU compute and lower the time to scientific insights.
If you have interest in NumPy, SciPy, Signal Processing, Simulation, DataFrames, or Graph Analysis, we'd love to hear what performance you're seeing and how you're measuring. We've been working to accelerate your favorite packages on GPUs